{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "train = pd.read_csv('./pima-indians-diabetes .csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 从中存在部分数据与常规不一致，分别是Glucose（葡萄糖浓度），BloodPressure（舒张压），SkinThickness（三头肌皮褶厚度），Insulin（胰岛素），BMI（体重指数）。这5维特征存在为零现象，需要特别处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、特征分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1、离散型分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （1）Pregnancies特征分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d30a373cc0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Pregnancies',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 图中表明大部分人怀孕次数在0-2之间，这与常规比较吻合。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （2）Age特征分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d30c4c3208>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Age',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 年轻女子分布比较多，年龄越大数据越少"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 、连续型特征分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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djohMglS6LT4E/BZYYmZHzexPzeyDZvZBb5bHgf3AXuC7wIcnrNoMa+uO30giF47QAVYsqOGjb13ET9cf5eeb8/ZHk4h4kl7675x7T5LpDvhI2irKYm3d2dtlcTgfu7aRF/e28qlHtnLWzHIW1ZdluiQRmSDZ2VUjSw1cVJQrR+gAQb+Pb7znQgqDPv70B6+8vg0ikn8U6KPQFupjWlGAgkBu/dlmV5XwnTuXc7yjlw8+sJ6+SDTTJYnIBMitZMqw1lCY2rLcaW5JdNG8Kr5y6/m8fLCdv31kK/GWMhHJJwr0UWgL9WXlGC6p+oPzz+Dj1zXyyIZjfOU3uzNdjoikmcZDT1FPOEp3OJrVR+jJxme/fcVc7rm2kROd8atIq0oK+LMrF0xSdSIy0RToKRroslibA33QR2Jm/MPN59DRE+YffrmTypICbrlodqbLEpE0UKCnqHVglMUsPkJPJvEIfuX8GvY0hfjrn21m85FTnDWznNtXzM1gdSIyXmpDT1FrqA8jt7osjiTg93HHirmcUVnMQy8f5kBrd6ZLEpFx0hF6itpCfVSUBAn68+c7sDDo565LG1j1/H4eWHOQoqCPmRXFI35GR/Ei2St/0mmCtXWHqc2hK0RTVVYY4E8ub6DA7+P+lw5yUhceieQsBXoKnHO0hvpyYlCusagsKeD9l8+nPxbjh2sO0tuvC49EcpECPQXd4Si9/bGs7rI4XtPLi7j9knm0dPXx41cOE9U9SUVyjgI9BW2h/OiymMyi+jJuOn8We06EeHxbXo6ALJLXdFI0BfnQZTFVF8+vpiXUx4t7W5lZXsTyhupMlyQiKdIRegraQn34DKpK8vsIfcAN58xgUV0Zj21+jaaO3kyXIyIpUqCnoDXUR1VJAX7fULdPzT8+M25dPpvioJ8fvXyYPp0kFckJCvQUtHXn7iiLYzWtKMi7L55DW6iPRzcd0+iMIjlAgZ7EQJfFfD8hOpQFdWVce9Z0Nh/tYMPhU5kuR0SSUKAncaKzj/6omxInRIdy9ZI65teW8ostr3HqtC46EslmCvQkBsY4ydeLipLxmfHHy2bjHPznRjW9iGQzBXoSB9vigT7V2tATVZcW8PZzZvBqc4j/eOVIpssRkWEo0JM40NpNwGdUFAczXUpGrZhfzYLaUv7hlzs5evJ0pssRkSEo0JPY3xKiurQAn02NLovDGWh6iTnH3z26TU0vIllIgZ7EnhMhppcXZbqMrFBVWsBfXL+YZ3a38MS2pkyXIyKDKNBHcDoc4cjJ09SXT93288Hed1kDS2eW87nHttPZ25/pckQkgQJ9BHubQzgH06fpCH1AwO/j//zRubSE+vh/v96d6XJEJIECfQR7ToQA1OQyyAVzKrlr5Tx+uOYQm47ogiORbKFAH8GrJ7ooCPjy5j6i6fSJty+hrqyQTz+6VWOni2QJBfoIdp/oYmFd2ZQZlGs0youCfOadS9l2rJMHfnsw0+WICBoPfUSvngixvKEq02VklR+tPfz6c+ccjfVl/NMTu+iNxCgvCuom0iIZpCP0YXT19nPsVA+Lp0/LdClZy8z4g/PPIBpzPL5VdzgSyTQF+jBebY6fEFWgj6ymrJC3LK5jy9EOXm3uynQ5IlOaAn0Yr56Ih9MSBXpSVy2uo6a0gP/a9Bo9Yd0MQyRTUgp0M7vBzHab2V4z++QQ0682sw4z2+Q9PpP+UifX7qYQxUE/s6uKM11K1gv6fdx84Szau8P881N7Ml2OyJSVNNDNzA98E7gRWAq8x8yWDjHrC865C7zH36e5zkn3anMXi+rL8KmHS0oW1pWxfF4V331hP9uOdWS6HJEpKZUj9EuAvc65/c65MPBj4KaJLSvzdjd1qf18lG48ZyY1ZYX89c+20B+NZbockSknlUCfBSQOgn3Ue2+wS81ss5k9YWZnD7UgM7vbzNaZ2bqWlpYxlDs5Ok7309zVx+LpZZkuJacUF/j5wk1ns+N4J999YX+myxGZclIJ9KHaHAZfGrgBmOecOx/4BvDoUAtyzq1yzi13zi2vq6sbXaWTaI/XW2PxDB2hj9YN58zkxnNm8LUn97Djtc5MlyMypaQS6EeBOQmvZwOvJc7gnOt0zoW8548DQTOrTVuVk2x3kxfoanIZk3/8w3OpKing4/+xkd5+9XoRmSypBPorQKOZzTezAuA24LHEGcxshln8DhBmdom33LZ0FztZXj3RRVlhgDMqNCjXWFSXFvDlW89nz4kQX3xiV6bLEZkykga6cy4CfBT4NbAT+IlzbruZfdDMPujNdguwzcw2A18HbnM5fEubnU1dNE4vw6b4XYrG4y2L63jfZQ3cv/ogz+3J3vMlIvkkpX7ozrnHnXOLnXMLnXP/6L13n3PuPu/5vc65s51z5zvnVjrnVk9k0RMpEo2x9WgH58+uzHQpOe+TN55JY30Zn/jJJpo6ejNdjkje05Wig+w5EaKnP8qFcxXo41UU9POtO5ZxOhzlQw+uJxxRV0aRiaTRFgfZeOQkEL+Jg4xe4miMA266YBYPvXyYu76/lh/ffWkGqhKZGnSEPsimw6eoLi1gbnVJpkvJG+fOquCKRbWs2d/Ow+uPZrockbylQB9k45FTXDCnUidE0+ztZ89gfm0pf/ufW1l/qD3T5YjkJQV6go6efvY2h7hQzS1p5/cZt18ylzMqivjzH67nUFt3pksSyTsK9ARbjsZveHzhXN2laCKUFgb4t/dfQsw53n//K5w6Hc50SSJ5RYGeYNPhU5jBeXMqMl1K3ppfW8qqO5dztL2HDzywXleSiqSRAj3BxiOnWFRXRnlRMNOl5LVL5lfz5VvPY+2Bdu758UaisZy9Bk0kq6jbosc5x6Yjp7j2zPpMl5LXErs1/t65M/nl1uPctuq33HzBLMxMN5kWGQcFuudw+2nau8NqP59Ely+qpbsvwrN7WigtCPC2s2dkuiSRnKZA92w8HD8hqguKJtf1S6cT8kK9MOjXEbrIOKgN3bPpyClKCvy6qcUkMzNuvnAW582u4Nfbm7j/pQOZLkkkZ+kI3bP+0EnOnVVBwK/vuMnmM+PWi+bQH3V87uc7KC7w8+6LdaQuMlpKL6C5s5etxzq4anH23kUp3/l9xnsunsNVi+v45CNb+ckrR5J/SETeQIEOPL2rGYDrzpqe4UqmtoDfx6o7L+LKxjr++uEtPLDmUKZLEskpCnTgqR0nmFNdrPbzLFAU9LPqzou49sx6/u7RbXzvRbWpi6Rqygf66XCEF/e2ct1Z0zUgV5YoCvr59nsv4oazZ/CFX+zgn57YSUwXH4kkNeUD/cVXW+mLxLhezS1ZpSDg497bL+S9K+fynef28+EHN9AT1jABIiOZ8r1cntp5gmlFAS6eX53pUoQ33yDjrBnl/N65M3l863Gu++pzvOeSuXz0mkUZqk4ku03pI/RozPH0zmbeuqSeoLorZiUz4/JFtbx35TxaQ318/elX+fc1h8jhe5CLTJgpnWKbjpyirTvMdUvV3JLtzppZzj3XNjK3poRPP7qNu77/MruaOjNdlkhWmdJNLk/uOEHAZ7xF/c9zQmVJAe+/rAEHfPGJXdzwzy9w3Vn1fPiti1iWMAbPUPc1TaThBSRfTdlAj8Ucv97exIoF1VQUa7jcXGFm3LFiLu887wzuX32Qf1t9gD/61mrm15by1iX1XHNmPX39UQqD/kyXKjLppmygP7XzBAdau7nn2sZMlyJjUFES5J7rGvmzK+fzyMZjPLXjBP++9hDf98aCqSoJMr28iNqyQipLglSVFFBdGn+I5KspGejOOb717D7mVBfz++fNzHQ5Mg6lhQHuXDmPO1fO43Q4wtr97Tz08mGaOns50dnLvpYQ/dHfnUA1YNXz+1kyYxrnzKrg3FkVXDi3ktqywsxthEiaTMlA/+3+NjYdOcUXbj5Hg3HlkZKCAG89s57jHb2vv+ecozsc5WR3mLbuMG2hPlpDfWw71sEzu5oZiPoZ5UUsqCtlUX0ZC+vK+B+XNWRkG0TGY0oG+ref3UdtWSG3XjQ706XIBDMzygoDlBUGmFNd8oZp4UiM1071cLCtm/0t3bx8oJ3V+9oI+o3n97RwzVn1XHvmdGZUFGWoepHRmXKBvvVoBy+82spf37CEIp04m9IKAj4aaktpqC3l6iXQH41xsLWbXU1dbDh8kqd3NfO/2casymLOnDGNJTOmcUZlMT7dKk+y1JQL9G89u5dphQHeu3JepkuRMUrWLXGsgn4fjdOn0Th9Gr9/3kyau/rYdbyTnU1d/PeuZp7e1UxpYYDF9WUUBnxcvqhWR++SVaZUoP/XpmM8sa2Jj12ziPIidVWU4ZkZ08uLmF5exFuW1BPqi7C3uYvdTV3sPtHFJ366GYAFdaWsmF/DygXVrFxQw/RyBbxkzpQJ9G3HOvibh7dwSUM1H71GXRVldMoKA1wwp4oL5lQRc44Tnb3sbQ6xv6WbRzYc5aGX478aakoLaKgt5fZL5rJiQTWzq0qSLFkkfaZEoLeF+vjAA+upKingm3csoyCgni0ydj4zZlYUM7OimCsb64jGHE0dvRxoDXGgtZsdr3W+fgQ/u6qYlQtqvIcCXiZW3gd6e3eYD/37BlpCffzsg5dSN039jSW9/D5jVlUxs6qKuaKxjphzXDSvirX721izv52nd57gZ+uPAvGAv6ShmovnV3NxQzULakvx+TQOv6RHXgf6L7cc5zP/tY3O3n6+cuv5nDe7MtMlyRTgM2Pj4VMUBPxctbiOKxprae7sY793BP/r7U08svEYANMKAyw9o5xzZ1WwqL6MeTWlzKspoX5aYcrXSDjnCEdj9EVihCMxIgkXUvkMCoN+ioI+Cvw+3cQlz6UU6GZ2A/AvgB/4V+fcFwdNN2/6O4DTwPuccxvSXGtK+iJRVu9t40cvH+bJHSc4d1YFD966gjNnlGeiHBF8ZsyoKGJGRRGXLazFOcelC2tYd/AkW46dYtuxTh5Yc4i+SOwNn5tWFKCyJEhJMIDPZ/gMnIv/Nx6OxjjZ3U84GqM/EiOVwYR9Fr8bVHHQT3GBn9KCAOfOrqCmrICa0gJqSgup9p4PDJVQUuDPyi8B5xx9kRi9/VEeevkI/dEYkZgjGnNEozFijtf/JgbceO4MAj4fhUEfxUE/RUE/JQX+rN2+sUoa6GbmB74JXA8cBV4xs8ecczsSZrsRaPQeK4Bve/+mnXOO0+Eoob4IXb0ROnrCHGmPXxyy50QXz+9pJdQXYVphgL96+xI+cNUCXQ0qWcXMWFBXxoK6Mt518RwAItEYxzt6OdR2mkPt3bR2hVlzoI2ecJR+L6Ci3m34igsClPuMGeVFFPh9BAPxo++Azwj4ffjMGIioGI7+qKM/Gj967+2P0tMfpSccpau3n5f2ttLWHSY86MtkgN+M4oL4F0BRwEeRF4aFAR+FAR/L5lW94XXQ78PnM/xm+HwQi0HMOWIuXkckGqM/+rtfFH2RKFuPdhCJOSJRRyQW/4URjTn6vefTigL09kfp7U+ovz/KaIbEX/XC/qH3BVAY9FFTWsi0ogDlxUEqioOUFwUpLw5QXhRkWlHAewS9L4EAJQXxXz2FAT+F3q+fgN9H0B/fdr/PMvJFkcoR+iXAXufcfgAz+zFwE5AY6DcBP3Txuw6sMbNKM5vpnDue7oJ/vuU4H3to45veN4MzKuJjs7z9nBlctrCGwoAuHJLcEPD7mFNdwpzqEq6gFoC6tRN/vuf2FXNxzhHqi9DeHaY11MfJ7n7au8O0nw6zem8bPf0RToejr4fpydPh15t3Vu9vG1WwDhZvBopvf8BnBHxG0O/D7zOCfqMg4GNmRVG82SgQD9GBXxgDvza2HO0g6LfXlxEPUxj4WnO4179YIjFHfyRGeOALLhKlrz/GzMoiOnsidPb2c6T9NJ09/XT2Rgj1Rca8bT6L/zozi3+JG/GcAvjzKxfwibctGfsfbhipBPos4EjC66O8+eh7qHlmAW8IdDO7G7jbexkys92jqjaJg8Bq4Euj/2gt0JrOWrKAtimL3fG7pxndpjuSzzIWebOfEqR1m/7Se4zRsFdFphLoQ/1uGPydnMo8OOdWAatSWOekMrN1zrnlma4jnbRNuUHblBtyZZtSaVw+CsxJeD0beG0M8ywCWOQAAAfOSURBVIiIyARKJdBfARrNbL6ZFQC3AY8Nmucx4C6LWwl0TET7uYiIDC9pk4tzLmJmHwV+Tbzb4vedc9vN7IPe9PuAx4l3WdxLvNvi+yeu5AmRdc1AaaBtyg3aptyQE9tkbjynqEVEJGuog7aISJ5QoIuI5IkpFehmdoOZ7TazvWb2ySGmm5l93Zu+xcyWZaLO0Uhhm642sw4z2+Q9PpOJOlNlZt83s2Yz2zbM9FzcR8m2Kaf2EYCZzTGzZ8xsp5ltN7N7hpgnp/ZVituU3fvKOTclHsRP6O4DFgAFwGZg6aB53gE8Qbxf/UpgbabrTsM2XQ38ItO1jmKbrgKWAduGmZ5T+yjFbcqpfeTVPBNY5j2fBuzJg/+fUtmmrN5XU+kI/fUhDJxzYWBgCINErw9h4JxbA1Sa2czJLnQUUtmmnOKcex5oH2GWXNtHqWxTznHOHXfeAHzOuS5gJ/GrwxPl1L5KcZuy2lQK9OGGJxjtPNkk1XovNbPNZvaEmZ09OaVNmFzbR6nK2X1kZg3AhcDaQZNydl+NsE2Qxfsqr8dDHyRtQxhkkVTq3QDMc86FzOwdwKPER8XMVbm2j1KRs/vIzMqAh4GPO+c6B08e4iNZv6+SbFNW76updISej0MYJK3XOdfpnAt5zx8HgmZWO3klpl2u7aOkcnUfmVmQePA96Jx7ZIhZcm5fJdumbN9XUynQ83EIg6TbZGYzzBuY2cwuIb7P2ya90vTJtX2UVC7uI6/e7wE7nXNfHWa2nNpXqWxTtu+rKdPk4vJwCIMUt+kW4ENmFgF6gNucd7o+G5nZQ8R7EtSa2VHgs0AQcnMfQUrblFP7yHM5cCew1cw2ee99CpgLObuvUtmmrN5XuvRfRCRPTKUmFxGRvKZAFxHJEwp0EZE8oUAXEckTCnQRkTyhQJc3MbOoN5Lcdu8S578wM583bbmZfT3J599nZveOcp2fGke995vZAa/mDWZ26Sg/H/L+PcPMfjbWOkaxvs+Z2bGEEfu+mObl32xmSxNe/72ZXZfOdUh2UrdFeRMzCznnyrzn9cCPgJecc59N8fPvA5Y75z46lnWOod77iY+A9zMzexvwFefceZO0br9zLjrKz3wOCDnnvjKWdaaw/Pvx/h4TsXzJXjpClxE555qBu4GPelf8XW1mv4D4lXJmttrMNnr/Lkn46Bwz+5XFx2p//YvAzN5rZi97R6bfMTO/d4Ra7L334Ajz+b2j8W1mttXM/tcQJT8PLPKWsdCrYb2ZvWBmZ3rvzzez35rZK2b2hYTaGswbs9zMSszsJxYfx/s/zGytmS33poW8o961xAdqusjMnvPW82vzRhQcbv3DMbOD5l1G7v0SetZ7/jmLj6n+rJntN7OPJXzmLq/GzWb2gJldBvwB8GXvb7fQ+5vd4s1/rbe/tnrLLExY9+e9Xzhbk9UqWSrT4/fqkX0P4kePg987CUwnYTxooBwIeM+vAx72nr8POA7UAMXANmA5cBbwcyDozfct4K7B6xxuPuAi4MmE+Sq9f+8HbvGe34o37jbwNNDoPV8B/Lf3/LGE9X5kYN1AA96Y5cBfAt/xnp8DRIj/6oD4AFPv8p4HgdVAnff63cSv2B1p/Z8DjgGbvMfbvfcPArXe8+XAswnzrwYKgVril5oHgbOB3QmfqR7890h8DRQRH/1wsff+D4kPQDWw7v/pPf8w8K+Z/u9Qj9E/psyl/zJuQ42cVwH8wMwaiYdcMGHak865NgAzewS4gngoXgS8YvHhMIqB5iGWe+0w8/0cWGBm3wB+Cfwm4TNfNrNPAy3An1p8xLzLgJ96y4B4IEL8Eu8/9p4/AHxpiBquAP4FwDm3zcy2JEyLEh/ACWAJ8cB/0luPHzieZP0AX3Oja3L5pXOuD+gzs2biX67XAD9zzrV6dSYbc30JcMA5t8d7/QPiX2j/7L0eGIxqPfBHo6hNsoQCXZIyswXEQ6yZ+NHzgC8Azzjn/tDi40c/mzBt8MkZR/xL4QfOub9Ntsrh5jOz84G3Ew+idwF/4k36K5fQZmxm5cAp59wFw6wj2cmjob7ABvS637WbG7DdOfeGE7EprH8oEX7XDFo0aFpfwvMo8f93jdENRzvSNiWuY2D5kmPUhi4jMrM64D7gXuf9Hk9QQbzpAOLNLImuN7NqMysGbgZeIt4EcYt3ohVv+jxv/n6LD13KcPN57cs+59zDwN8Rv63bkFx8HOsDZnartwzzvgzwarnNe37HMIt4kfgXBhbvMXLuMPPtBurM61ljZkEzOzvJ+odzkPgvE/jdL4iRPA28y8xqvHVUe+93Eb+F2mC7gAYzW+S9vhN4LoX1SI5QoMtQBk5QbgeeIt608fkh5vu/wD+Z2UvEmxoSvUi8OWMT8bb1dc65HcCngd94TRhPEr+PI8AqYIuZPTjCfLOAZy0+Et79QLIj/TuIN79sBrbzu9vz3QN8xMxeIf6lNJRvEQ/qLcDfAFuAjsEzufit/24BvuStZxPxppaR1j+czwP/YmYvED9KHpFzbjvwj8Bz3joGhnz9MfBX3snPhQnz9xIf8fCnZrYViBH/spY8oW6LIkMwMz/xk7K9Xig+TfxkYjjDpYkMS+1kIkMrAZ7xmoEM+JDCXLKdjtBFRPKE2tBFRPKEAl1EJE8o0EVE8oQCXUQkTyjQRUTyxP8HkXNb28SLzDEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "special_ = ['Glucose','BloodPressure','SkinThickness','Insulin',\n",
    "                       'BMI','DiabetesPedigreeFunction']\n",
    "for every in special_:\n",
    "    _,ax = plt.subplots()\n",
    "    sns.distplot(train[every],bins=30,ax=ax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 与之前一样，存在部分数据异常情况（置零）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、特征与标签分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1、离散型与标签关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d30c6c6d68>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hVn5uKZiZWcZFwczMMi4KZmaWcVEwM7OMi4KZmWVcFMzMLOOiYGZmGRcFMzPLuCiYmVnGZzQXwBftMbNa5ZaCmZllXBTMzCzjomBmZhnvU6gx3t9hZsVwS8HMzDIuCmZmlnFRMDOzjPcpmKW8P8bMLQUzM8vhomBmZhkXBTMzy3ifgrXL/exm9cUtBTMzy7ilYNZJ3MqyfVHZWwqS+kj6V0krJS2XNCld3l3SU5JWpfdHlDs3M7N6V4mWwg7g6xGxSNKhwEJJTwHjgDkRcaukycBk4BsVyM86kX89m1W3srcUImJ9RCxKp/8ErASOBkYD09KHTQPOK3duZmb1rqL7FCT1BU4Gfgf0ioj1kBQOST33sM0EYALAMcccU55ErSYMu356u+sfO7RMiZhVsYodfSTpEOAR4GsR8Va+20XE1IhojojmxsbGzkvQzKwOVaSlIKkbSUGYERGPpos3SOqdthJ6AxsrkVsltNfP7j52MyunShx9JOBuYGVE3Jaz6glgbDo9Fni83LmZmdW7SrQURgCXAEslLU6X/U/gVuBBSZcDa4ALKpCbWVXyUVtWLmUvChHx74D2sHpkOXMxM7MP8jAXZmaWcVEwM7OMxz6yfVal+9l93oPVIrcUzMws46JgZmYZdx+Z1aC9dW0tnHJpmTKxfY1bCmZmlnFRMDOzjIuCmZllXBTMzCzjomBmZhkXBTMzy7gomJlZxucpmFWJ9s4tKPWQGZUeIsSql1sKZmaWcVEwM7OMi4KZmWVcFMzMLOOiYGZmGR99ZGZF89FMtcMtBTMzy7gomJlZxkXBzMwy3qdgZmVXyivD7S3WY4dOaXe993d8kFsKZmaWcUvBzPZq77/GS/t87R3N5F/2ncstBTMzy7gomJlZxkXBzMwy3qdgZtYBtX72dtW1FCSdJelFSS9LmlzpfMzM6klVtRQkdQF+DJwBtAC/l/RERKyobGZmZp2jlEdalaIVU20thU8CL0fEHyJiG/AAMLrCOZmZ1Q1FRKVzyEg6HzgrIq5I5y8BPhURE3MeMwGYkM4eD7yYR+gewOYSplrN8ao5t1LHq+bcSh2vmnOr9njVnFup4+Ub6y8jorGtFVXVfQSojWUfqFoRMRWY2qGg0oKIaC4msX0lXjXnVup41ZxbqeNVc27VHq+acyt1vFLEqrbuoxagT858E/BahXIxM6s71VYUfg8MkNRP0v7AhcATFc7JzKxuVFX3UUTskDQR+BXQBbgnIpaXIHSHupv28XjVnFup41VzbqWOV825VXu8as6t1PGKjlVVO5rNzKyyqq37yMzMKshFwczMMjVfFEo5bIakeyRtlLSsBHn1kfSvklZKWi5pUpHxGiTNl7QkjXdTCXLsIuk5SU+WINZqSUslLZa0oATxPiLpYUkvpH/DU4qIdXya167bW5K+VkS8a9L/wTJJMyU1FBorjTcpjbW8kLzaet9K6i7pKUmr0vsjiox3QZrfTkl5HxK5h1hT0v/r85Iek/SRIuP9XRprsaTZko4qJl7OuuskhaQeReT2HUnrct57ZxeTm6R/yom1WtLifONlIqJmbyQ7q/8T6A/sDywBTiwi3mnAUGBZCXLrDQxNpw8FXioyNwGHpNPdgN8Bw4vM8Vrg58CTJXi9q4EeJfzfTgOuSKf3Bz5SwvfM6yQn9xSy/dHAK8CB6fyDwLgi8vk4sAw4iOTAkF8DAzoY40PvW+B/AZPT6cnA94uMdwLJyaTzgOYiY30B6JpOf78EuR2WM3018H+KiZcu70NyQMyr+b6v95Dbd4DrCnxvtPt9BPwQ+FZH49Z6S6Gkw2ZExG+AN0qRWESsj4hF6fSfgJUkXyiFxouIeDud7ZbeCj6KQFIT8NfATwuN0VkkHUbygbgbICK2RcT/K1H4kcB/RsSrRcToChwoqSvJl3kx59qcADwbEVsjYgfwNPCljgTYw/t2NElhJb0/r5h4EbEyIvIZXSCfWLPT1wrwLMn5SsXEeytn9mA68Llo5zN/O3BDiWIVpL14kgR8BZjZ0bi1XhSOBtbmzLdQxBdvZ5HUFziZ5Nd9MXG6pM3FjcBTEVFMvH8gedPvLCanHAHMlrQwHaqkGP2BTcC9affWTyUdXHyKQHJuTIc/SLtExDrgB8AaYD3wx4iYXUQ+y4DTJB0p6SDgbD54gmehekXEekh+oAA9SxCzM4wHfllsEEk3S1oLjAG+VWSsUcC6iFhSbF6piWn31j0d6cbbi78CNkTEqo5uWOtFYa/DZlSapEOAR4Cv7faLpsMiojUiTiL5ZfVJSR8vMKdzgI0RsbCYfHYzIiKGAl8ErpJ0WhGxupI0m38SEScDfybpAilKesLkKOChImIcQfIrvB9wFHCwpIsLjRcRK0m6UJ4CZpF0ge5od6MaIelGktc6o9hYEXFjRPRJY03c2+Pbyekg4EaKLCw5fgIcC5xE8iPihyWKexEF/rip9aJQ1cNmSOpGUhBmRMSjpYqbdqXMA84qMMQIYJSk1SRdbp+X9LMic3otvd8IPEbStVeoFqAlpyX0MEmRKNYXgUURsaGIGKcDr0TEpojYDjwKnFpMUhFxd0QMjYjTSLoLOvzrrw0bJPUGSO83liBmyUgaC5wDjIm0g7xEfg78lyK2P5ak4C9JPx9NwCJJf1FIsIjYkP6Y2wn8X4r7XACQdlt+GfinQrav9aJQtcNmpH1+dwMrI+K2EsRr3HWUhqQDSb6cXigkVkR8MyKaIqIvyd9sbkQU/GtX0sGSDt01TbIjseAjuCLidWCtpOPTRSOBUlxzo+BfVznWAMMlHZT+j0eS7C8qmKSe6f0xJB/2YnOE5HMwNp0eCzxegpglIeks4BvAqIjYWoJ4A3JmR1Hg5wIgIpZGRM+I6Jt+PlpIDhh5vcDceufMfokiPhc5TgdeiIiWgrYuZK/3vnQj6YN9ieQopBuLjDWTpIm3neTNcHkRsT5N0pX1PLA4vZ1dRLzBwHNpvGUUcNTBHuJ+liKPPiLZB7AkvS0v9v+QxjwJWJC+3l8ARxQZ7yBgC3B4CXK7ieSLZxlwP3BAkfH+jaToLQFGFrD9h963wJHAHJJWxxyge5HxvpROvwdsAH5VRKyXSfYF7vpcdORoobbiPZL+L54H/hk4uph4u61fTf5HH7WV2/3A0jS3J4DexeYG3Ad8tdD3m4e5MDOzTK13H5mZWQe4KJiZWcZFwczMMi4KZmaWcVEwM7OMi4LVPEmt6aiRyyQ9lJ6Vuk+Q9B+VzsHqi4uC1YN3IuKkiPg4sA34au5KJarysxARRZ0NbdZRVflBMOtE/wYcJ6mvkusw3AUsAvpI+oKkZyQtSlsUhwBIOjsd3//fJd2p9PoS6Vj490iaJ+kPkq7e9SSSfpEO/rc8dwBASW+ng7MtkfSspF7p8l5Krh2wJL2duuvxOdteL+n36eBpN6XLDpb0L+k2yyT91zL8Da2GuShY3UjHhPkiyRmkkIz/Pz3eH1Tvb4HTIxm4bwFwrZIL5Pwj8MWI+DTQuFvYjwFnkoxZ8+10PCuA8RExDGgGrpZ0ZLr8YJKhsIcAvwH+Jl1+J/B0unwoyZnfubl/ARiQPs9JwLB0UMGzgNciYkjaEppV+F/IzEXB6sOB6ZDiC0jGJro7Xf5qRDybTg8HTgR+mz52LPCXJF/6f4iIV9LH7T7u0L9ExHsRsZlkULle6fKrJS0huR5AH5IvdEi6r3ZdyW4h0Ded/jzJiJlEMkDaH3d7ni+kt+dIWjYfS2MuBU6X9H1Jf9XGdmYd0rXSCZiVwTuRDCmeScaq48+5i0iuQXHRbo87eS+x38uZbgW6SvosyaBkp0TEVknzgF2X5Nwe748t00r+n0EBfx8R//ihFdIwkjG+/l7S7Ij4bp4xzT7ELQWzxLPACEnHQTJuvqSPkgxs1z+9EBJAPn32hwNvpgXhYyStkL2ZA1yZPncXJVeXy/UrYHzOfo6jJfVUcr3hrRHxM5KL+5RiCHGrY24pmAERsUnSOGCmpAPSxX8bES9J+u/ALEmbgfl5hJsFfFXS88CLJAVnbyYBUyVdTtKCuBJ4Jie/2ZJOAJ5JWzlvAxcDxwFTJO0kGS3zyjyey2yPPEqq2V5IOiQi3k6vj/BjYFVE3F7pvMw6g7uPzPbub9Kdz8tJuoY+1K9vVivcUjAzs4xbCmZmlnFRMDOzjIuCmZllXBTMzCzjomBmZpn/D9Mtynrf1AXUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Pregnancies',hue='Outcome',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 随着怀孕次数的增多，患病几率增加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d30c936ac8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Age',hue='Outcome',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 年龄也是如此"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2、连续型特征与标签分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d30cb20710>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# corrMatt = train[special_+['Outcome']].corr()\n",
    "corrMatt = train.corr()\n",
    "sns.heatmap(corrMatt,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 超过0.5的只有怀孕次数与年龄的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 之前指明存在部分特征数据异常情况，先做处理，这里 使用中值进行替换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 111\n",
       "Glucose                       5\n",
       "BloodPressure                35\n",
       "SkinThickness               227\n",
       "Insulin                     374\n",
       "BMI                          11\n",
       "DiabetesPedigreeFunction      0\n",
       "Age                           0\n",
       "Outcome                     500\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "(train == 0).sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  上述结果中，存在6个特征为零，其中怀孕次数可以为零，这里不做处理，剩余5种特征进行处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "special_feature = ['Glucose','BloodPressure','SkinThickness','Insulin',\n",
    "                  'BMI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[special_feature] = train[special_feature].replace(0,train[special_feature].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 111\n",
       "Glucose                       0\n",
       "BloodPressure                 0\n",
       "SkinThickness                 0\n",
       "Insulin                       0\n",
       "BMI                           0\n",
       "DiabetesPedigreeFunction      0\n",
       "Age                           0\n",
       "Outcome                     500\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train == 0).sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1、数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler()\n",
    "train_y = train['Outcome']\n",
    "train_x = train.drop(['Outcome'],axis=1)\n",
    "train_x = ss.fit_transform(train_x)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "params = {\"C\":[0.01,0.1,0,1,10,100],\"penalty\":['l1','l2']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 log似然损失评分标准方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='auto',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='liblinear',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': [0.01, 0.1, 0, 1, 10, 100],\n",
       "                         'penalty': ['l1', 'l2']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='liblinear')\n",
    "grid_log_loss = GridSearchCV(lr,params,scoring=\"neg_log_loss\",cv=5)\n",
    "grid_log_loss.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4744372124801874"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_log_loss.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_log_loss.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3、正确率评分标准方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n",
      "G:\\ProgramFiles\\anaconda\\envs\\csdnLearn\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: b'C <= 0'\n",
      "\n",
      "  FitFailedWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='auto',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='liblinear',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': [0.01, 0.1, 0, 1, 10, 100],\n",
       "                         'penalty': ['l1', 'l2']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(solver=\"liblinear\")\n",
    "grid_accuracy = GridSearchCV(lr,params,scoring=\"accuracy\",cv=5)\n",
    "grid_accuracy.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4、总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neg_log_loss评分标准得分：-0.4744372124801874\n",
      "accuracy评分标准得分：0.7695611577964518\n"
     ]
    }
   ],
   "source": [
    "print(f\"neg_log_loss评分标准得分：{grid_log_loss.best_score_}\")\n",
    "print(f\"accuracy评分标准得分：{grid_accuracy.best_score_}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neg_log_loss的best_estimator_：LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n",
      "accuracy的best_estimator_：LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(f\"neg_log_loss的best_estimator_：{grid_log_loss.best_estimator_}\")\n",
    "print(f\"accuracy的best_estimator_：{grid_accuracy.best_estimator_}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 负log似然函数得分是-0.4744，accuracy得分是76.96%。本次特征工程的时候对缺失值进行中值替换，也可以使用均值等进行替代，同时其中2个特征的缺失比较多，这种全盘中值替换处理会造成数据不稳，另一种处理方式是，用一种分类特征进行代替。此外，这两种评分标准的分数不在一个量纲上，无法比较。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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